Warning: DoE is now part of BoFire. This repository is no longer maintained, please use BoFire for the latest version.
DoE is a python package for generating (D-)optimal experimental designs. It uses opti and formulaic for specifying the design space and model.
You can find the documentation here.
DoE can be installed with
pip install git+https://github.com/basf/doe.git Please make sure to have cyipopt installed. With conda the easiest way to get this package is using
conda install -c conda-forge cyipopt See this link for more information on other ways to install cyipopt.
import opti import doe problem = opti.Problem( inputs = opti.Parameters([opti.Continuous(f"x{i+1}", [0, 1]) for i in range(3)]), outputs = [opti.Continuous("y")], constraints = [ opti.LinearEquality(names=["x1","x2","x3"], rhs=1), opti.LinearInequality(["x2"], lhs=[-1], rhs=-0.1), opti.LinearInequality(["x3"], lhs=[1], rhs=0.6), opti.LinearInequality(["x1","x2"], lhs=[5,4], rhs=3.9), opti.LinearInequality(["x1","x2"], lhs=[-20,5], rhs=-3) ] ) design = find_local_max_ipopt(problem, "linear")
- David Hajnal
- Jorge Diaz